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arXiv2026

Context-Aware Emotionally Adaptive Voice Assistants: A Multimodal Framework for Empathetic Human-Agent Interaction

Voice-assistant interruptions tend to be intrusive because existing systems fail to consider the affective state, cognitive load and situational context of the user when deciding when and how to interrupt.Voice-assistant interruptions tend to be intrusive, since existing systems do not consider the affective state, cognitive load or situational context of the user when determining when and how to interrupt. In this paper, EmpathicVA, a closed-loop framework integrating physiological sensing, vocal-affect analysis, contextual modeling and reinforcementlearning interruption policy, is introduced. A hierarchical fusion model involves integrating HRA, EDA, respiration, acousticprosodic features, linguistic embeddings, and contextual cues and computing the probabilities of five affective states. A Double Deep Q-Network selects immediate response, brief or extended delay, empathetic response, or silent mode based on these probabilities, context and interaction history. The multimodal model obtained an accuracy of 92.3% and an F1-score of 0.922 at the macro level on a held-out test set, outperforming the highest accuracy unimodal model by 6.0 percentage points. Comparing the six-week within-subject field study with 48 participants with a baseline and context-only assistants, there was a corresponding increase in satisfaction, trust, and appropriateness of timing, as well as a large reduction in interruption-related stress episodes. The results suggest that affect-aware timing and restraint are both important in voice interaction in addition to the response wording.

cs.HC↗

arXiv2026

XMACNet: An Explainable Lightweight Attention based CNN with Multi Modal Fusion for Chili Disease Classification

Plant disease classification via imaging is a critical task in precision agriculture. We propose XMACNet, a novel light-weight Convolutional Neural Network (CNN) that integrates self-attention and multi-modal fusion of visible imagery and vegetation indices for chili disease detection. XMACNet uses an EfficientNetV2S backbone enhanced by a self-attention module and a fusion branch that processes both RGB images and computed vegetation index maps (NDVI, NPCI, MCARI). We curated a new dataset of 12,000 chili leaf images across six classes (five disease types plus healthy), augmented synthetically via StyleGAN to mitigate data scarcity. Trained on this dataset, XMACNet achieves high accuracy, F1-score, and AUC, outperforming baseline models such as ResNet-50, MobileNetV2, and a Swin Transformer variant. Crucially, XMACNet is explainable: we use Grad-CAM++ and SHAP to visualize and quantify the models focus on disease features. The models compact size and fast inference make it suitable for edge deployment in real-world farming scenarios.

cs.CV↗